public LargeMeanPoissonSampler()

in commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.java [126:162]

• 30 lines of code
• 4 McCabe index (conditional complexity)

    public LargeMeanPoissonSampler(UniformRandomProvider rng,
                                   double mean) {
        if (mean < 1) {
            throw new IllegalArgumentException("mean is not >= 1: " + mean);
        }
        // The algorithm is not valid if Math.floor(mean) is not an integer.
        if (mean > MAX_MEAN) {
            throw new IllegalArgumentException("mean " + mean + " > " + MAX_MEAN);
        }
        this.rng = rng;

        gaussian = ZigguratSampler.NormalizedGaussian.of(rng);
        exponential = ZigguratSampler.Exponential.of(rng);
        // Plain constructor uses the uncached function.
        factorialLog = NO_CACHE_FACTORIAL_LOG;

        // Cache values used in the algorithm
        lambda = Math.floor(mean);
        logLambda = Math.log(lambda);
        logLambdaFactorial = getFactorialLog((int) lambda);
        delta = Math.sqrt(lambda * Math.log(32 * lambda / Math.PI + 1));
        halfDelta = delta / 2;
        sqrtLambdaPlusHalfDelta = Math.sqrt(lambda + halfDelta);
        twolpd = 2 * lambda + delta;
        c1 = 1 / (8 * lambda);
        final double a1 = Math.sqrt(Math.PI * twolpd) * Math.exp(c1);
        final double a2 = (twolpd / delta) * Math.exp(-delta * (1 + delta) / twolpd);
        final double aSum = a1 + a2 + 1;
        p1 = a1 / aSum;
        p2 = a2 / aSum;

        // The algorithm requires a Poisson sample from the remaining lambda fraction.
        final double lambdaFractional = mean - lambda;
        smallMeanPoissonSampler = (lambdaFractional < Double.MIN_VALUE) ?
            NO_SMALL_MEAN_POISSON_SAMPLER : // Not used.
            KempSmallMeanPoissonSampler.of(rng, lambdaFractional);
    }